Acoustic to Articulatory Speech Inversion for Children with Velopharyngeal Insufficiency

Carol Espy-Wilson; Liran Oren; Mark Tiede; Saba Tabatabaee; Suzanne Boyce

REVIEW 3 major objections 6 minor 1 cited by

Acoustic to Articulatory Speech Inversion for Children with Velopharyngeal Insufficiency

T0 review · 3 major / 6 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A multi-task speech-inversion system estimates nasalance from audio 16.92% more accurately than a prior baseline, and fine-tuning on children with velopharyngeal insufficiency adds another 7.90% improvement.

desk verdict A reasonable first SI system for VPI children, but the headline PPMC gains rest on tiny test sets and no significance tests, so they are not yet established as real. read the letter →

arxiv 2509.09489 v1 pith:GNJR3JWU submitted 2025-09-11 eess.AS

Saba Tabatabaee , Suzanne Boyce , Liran Oren , Mark Tiede , Carol Espy-Wilson This is my paper

classification eess.AS

keywords speechinversionnasalancevelopharyngealinsufficiencyself-supervisedlearningmulti-taskelectroglottographyfine-tuningchildren's

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a speech-inversion system can estimate nasalance (the velopharyngeal port tract variable) directly from acoustic speech, using self-supervised embeddings combined with glottal source features. The authors report a 16.92% relative improvement in correlation over a previous system when tested on healthy adults, and a further 7.90% improvement after fine-tuning on a small set of children with velopharyngeal insufficiency (VPI). If correct, this offers a non-invasive, instrument-free way to monitor VPI in children, replacing uncomfortable procedures like nasopharyngoscopy and nasometry. The key is that multi-task learning with glottal features and variable-length training segments improves accuracy, and that a small amount of in-domain child speech is enough to adapt the model.

What carries the argument

The central mechanism is a multi-task learning framework that stacks representations from all 25 hidden layers of a self-supervised speech model (WavLM-Large) and passes them through bidirectional GRUs to simultaneously predict the VP tract variable (nasalance) and three source features (F0, periodicity, aperiodicity) plus the EGG envelope. The loss is a weighted combination of Pearson correlation and RMSE (α=0.2). Variable-length random segments of 2-5 seconds replace the fixed 2-second segments used previously, improving robustness to duration variation. For children, the EGG-dependent head is removed and the model is fine-tuned on VPI-child speech.

What would settle it

Re-run the same experiments with per-speaker bootstrapped confidence intervals for the PPMC differences between baseline and proposed systems; if the intervals overlap at the 95% level on either the adult test set or the VPI-child folds, the claimed relative improvements (16.92% and 7.90%) are not statistically reliable.

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Extended reading notes

Core claim

The paper's central claim is that a multi-task speech-inversion architecture—using WavLM-Large self-supervised embeddings, variable-length training segments (2-5 seconds), and auxiliary prediction of electroglottograph envelope, F0, periodic energy, and aperiodic energy—estimates nasalance with higher Pearson correlation than prior systems. On the healthy-adult test set, this reaches a PPMC of 0.9488, a 16.92% relative improvement over the baseline system of Siriwardena et al. Fine-tuning the adult-trained model on 14 children with VPI (5-fold cross-validation) raises the average PPMC from 0.6357 to 0.6859, a 7.90% relative improvement, and outperforms training from scratch on the child data

Load-bearing premise

The reported performance improvements are computed on very small test sets—two healthy adult speakers for the adult comparison, and only three children per fold for the VPI fine-tuning—with no significance testing or confidence intervals, so the differences could reflect speaker-specific variation rather than true model improvement.

Editorial extensions

If this is right

If confirmed, this system could enable at-home or remote monitoring of hypernasality and nasal emission in children with VPI, using only a smartphone microphone.
The two-stage training recipe—adult pretraining then fine-tuning on a small clinical dataset—provides a template for adapting speech-inversion models to other disordered or pediatric populations with limited data.
Including glottal source features (EGG, F0, periodicity, aperiodicity) as auxiliary tasks consistently improves nasalance estimation, suggesting vocal-fold dynamics carry complementary information about velopharyngeal function.
The fine-tuned system tracks nasality peaks corresponding to nasal emissions and hypernasal vowels, indicating potential clinical use for locating specific articulatory events in a child's speech.
Because training from scratch on the small VPI-child dataset yields far lower correlation (0.6388), the adult pretraining is shown to be essential, supporting the value of large healthy-speech corpora for clinical adaptation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not isolate how much of the adult improvement comes from the multi-task glottal features versus the variable-length segment strategy versus the choice of WavLM-Large; a controlled ablation varying these factors independently would sharpen the finding.
Since the EGG signal is unavailable for children, the practical system relies on acoustically derived source features; the relatively small performance drop when EGG is excluded (0.9488 vs. 0.9470 in the adult ablation) suggests EGG may be a convenient but not essential auxiliary input.
The cross-corpus generalization demonstration uses a single sentence; a more varied corpus would test whether the system truly generalizes beyond the training passages or overfits to their phonetic content.
The clinical value would be higher if the system not only tracks nasalance but also reliably distinguishes hypernasality from audible nasal emission—categories the authors mention but do not evaluate separately.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is the first attempt I know of to estimate nasalance directly from audio in children with VPI via a speech-inversion system, and it is a sensible engineering step. The authors build on strong prior work—WavLM-Large embeddings, multi-task learning with source features, and the same test set used by Siriwardena et al. [5]—and they include a useful ablation showing that glottal auxiliary tasks help VP TV estimation. The qualitative figures, especially the nasal rustle and hypernasality examples, show the model is tracking something meaningful. The speaker-independent splits and 5-fold CV for the child data are the right instincts.

The load-bearing problem is that the two headline quantitative claims are not actually supported by the numbers as reported. The 16.92% relative improvement over [5] is on a 2-speaker adult test set and conflates the new architecture with a larger training set (the original [5] result was trained on fewer speakers); the fair comparison against a retrained baseline gives 6.56%, still on 2 speakers. Even that could be driven by one speaker—no per-speaker scores are given. For the VPI fine-tuning result, the mean PPMC gain of 0.0502 is smaller than the reported fold-to-fold SD of the pre-fine-tuned model (0.14). With 5 folds and only 3 test children per fold, that difference is well within sampling noise. No significance test or confidence interval is reported anywhere, so the abstract's word "significant" lacks statistical backing.

There are also minor grievances: they normalize features using whole-dataset statistics including the test split, which is sloppy though it likely doesn't affect PPMC (affine transforms leave Pearson r invariant). No code or data is released, which makes it hard to reproduce or build on. None of this is fatal to the core idea—the paper is an application first, and the from-scratch child-only comparison (0.6388 vs 0.6859) at least offers an internal sanity check. But the clinical promise depends on a real, reliable fine-tuning gain, and this evaluation does not demonstrate that yet.

Who gets value from this? People working on articulatory inversion or clinical voice assessment will want to know this exists, and the method is a reasonable baseline for future work. I would encourage a serious referee to engage with it, primarily to push for per-speaker results, fold-level paired tests, and ideally an external validation set with more speakers. The field benefits from this being reviewed rather than desk-rejected; the science just needs the evidence to catch up with the claim.

Referee Report

3 major / 6 minor

Summary. The paper proposes a speech-inversion (SI) system that estimates nasalance (treated as a VP tract variable, VP TV) from audio, using self-supervised learning (SSL) embeddings (HuBERT-Large, wav2vec 2.0-Large, WavLM-Large), a multi-task objective that jointly estimates VP TV, an EGG-envelope proxy, and three glottal source features (F0, periodicity, aperiodicity), and variable-length training segments. The system is first trained on healthy-adult speech and then fine-tuned on a small dataset of children with velopharyngeal insufficiency (VPI). The paper reports a 16.92% relative PPMC improvement over the prior SI system of Siriwardena et al. [5] on the healthy-adult test set, and a 7.90% relative PPMC improvement after fine-tuning on VPI-child data, with additional qualitative cross-corpus and nasal-emission analyses.

Significance. If the reported gains are reliable, the work has clear clinical value: it is, to my knowledge, the first SI system aimed at estimating nasalance directly in children with VPI, and it provides a non-invasive alternative to nasometry and nasopharyngoscopy. The use of WavLM-Large embeddings for nasalance estimation, the multi-task auxiliary glottal-feature objective, and the adult-to-child fine-tuning strategy are all reasonable extensions of prior work. The paper has several good practices: it evaluates on the same healthy-adult test set as [5], it reports a retrained baseline on the current training data, and it includes an ablation of the auxiliary tasks. However, the central quantitative claims currently rest on very small test sets with no uncertainty quantification, and the headline adult improvement mixes multiple changes at once. These issues are fixable with additional analyses, but they are load-bearing for the paper's main conclusions.

major comments (3)

[§IV-A, Table II] The headline 16.92% relative improvement is not a controlled comparison. It compares the proposed WavLM system to the baseline result originally reported in [5], which was trained on the older, smaller healthy-adult corpus, whereas the proposed system is trained on the enlarged corpus (four additional subjects). The more controlled comparison in the same table—the same baseline architecture retrained on the current training data—yields only a 6.56% relative improvement. The abstract and conclusions nevertheless emphasize the 16.92% figure. Please make the controlled comparison the primary reported result and clearly separate the effect of added training data from the effect of the proposed model changes.
[§IV-A and §IV-D, Tables II and IV] Neither of the two central claims is supported by uncertainty quantification. The healthy-adult test set consists of only 2 speakers and 70 utterances (Table I); no per-speaker or bootstrap results are given, so the gain from 0.8115 to 0.9488 could be driven by one speaker. For the VPI-child fine-tuning, Table IV reports VP PPMC increasing from 0.6357 (SD 0.14) to 0.6859 (SD 0.08) over five folds, a mean difference of 0.0502 that is smaller than the reported standard deviation of the pre-fine-tuned model. With only 3 test speakers per fold, the 7.90% relative improvement is within fold-to-fold and speaker-level noise. Please report per-fold and per-speaker scores, confidence intervals (e.g., bootstrap over speakers), and a paired significance test before claiming in the conclusions that fine-tuning 'significantly improved' nasalance estimation.
[§III-B and §IV-A, Tables II and III] The claimed adult improvement conflates several simultaneous changes relative to [5]: the SSL embedding (HuBERT-Large vs. WavLM-Large), the multi-task auxiliary objective, the variable-length segment strategy, and the enlarged training corpus. Table III ablates only the auxiliary tasks within the WavLM system; it does not isolate the SSL model choice or the segment-length strategy. The conclusion that the 'proposed multi-task SI architecture and training strategy' is responsible for the improvement is therefore not supported by the experiments as presented. Please add single-factor ablations (e.g., WavLM with the baseline fixed-segment/task setup; HuBERT with the proposed multi-task variable-length setup). The ablation also shows that the glottal source features themselves contribute only a small absolute gain (VP PPMC 0.9444 without them vs. 0.9488 with them, Table III), which should be

minor comments (6)

[Abstract] Typo: 'an 7.90%' should be 'a 7.90%'.
[§III-A, Eq. (2)] The sentence 'In equation 1, α was set to 0.2' should refer to equation 2, where α is defined.
[§III-B1] Typo: 'later used to adopt the SI systems' should be 'later used to adapt the SI systems'.
[§III-A] State clearly whether the min-max normalization to [-1,1] is computed before or after the speaker-independent split. If computed over all utterances including the test set, this is a mild form of test-set leakage; it does not affect PPMC (which is affine-invariant) but it does affect RMSE-based training and should be described.
[§IV-C, Figure 3] The cross-corpus generalization claim is based on a single utterance and is only qualitative. It would be more appropriate to label this as an illustrative case study rather than a generalization evaluation, or to add a small quantitative summary over more utterances.
[References] Reference [22] appears incomplete/malformed ('vol. 0, no, 1994'). Please fix.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SI model is a supervised regression from mixed audio to an independently computed nasalance ratio, with no parameter fitted to the test target.

full rationale

The paper's central derivation is an empirical mapping from a combined audio signal (oral+nasal microphones) to a nasalance target computed as AEnasal/(AEnasal+AEoral). The target is not defined in terms of the model's output, and the model's input is a mixture, not the separated channels, so predicting the ratio from the mixture requires genuine source separation/regression rather than algebraic identity. The multi-task auxiliary outputs (EGG-env, F0, periodicity, aperiodicity) are additional supervised targets, not inputs; no parameter is fitted to the test labels. The comparison against Siriwardena et al. [5] uses an external published baseline and also retrains that architecture on the same data, so the self-citation is not load-bearing. Fine-tuning evaluation uses speaker-independent folds with unseen test speakers. The small test-set size and lack of significance testing are statistical-robustness concerns, not circularity. No quoted passage exhibits a reduction of a prediction to the model's own inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim relies on a supervised learning setup; the only fitted numbers are hyperparameters and the dataset normalization. No new physical or theoretical entities are introduced; VP TV is a rename of nasalance.

free parameters (3)

alpha (loss weighting) = 0.2
Set to 0.2 based on empirical grid search over alpha values (Section III-B).
learning rate and batch size = 5e-4 and 8
Selected via grid search (Section III-B).
per-dataset normalization min/max = unknown
Nasalance and EGG-env normalized to [-1,1] across all utterances in each dataset, including the test split (Section III-A). This is fitted to the whole dataset and is a mild leakage risk.

assumptions (4)

domain assumption SSL representations (HuBERT, Wav2Vec, WavLM) contain sufficient articulatory information for nasalance estimation.
Assumed from prior work [5], [9]; the paper does not verify this independently.
domain assumption Nasalance computed from separated oral/nasal microphone energies is a valid ground truth for velopharyngeal port constriction.
Used as target in Equation 1; HSN is said to assess quality but no quantitative validation is shown (Section III-A).
domain assumption Multi-task learning with glottal auxiliary predictions improves the primary nasalance estimation.
Borrowed from [7], [12]; ablation shows small gains but no significance test (Table III).
domain assumption Fine-tuning with a small set of disorder-specific child speech improves transfer over the adult model.
Standard transfer-learning assumption; evaluated with 5-fold CV but with high variance (Table IV).

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Cite this review

Pith. "Pith review of Acoustic to Articulatory Speech Inversion for Children with Velopharyngeal Insufficiency." pith.science (2026). https://pith.science/paper/GNJR3JWU

@misc{pith2026250909489,
  author       = {Pith},
  title        = {Pith review of: Acoustic to Articulatory Speech Inversion for Children with Velopharyngeal Insufficiency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GNJR3JWU}},
  note         = {Machine review of arXiv:2509.09489}
}

read the original abstract

Traditional clinical approaches for assessing nasality, such as nasopharyngoscopy and nasometry, involve unpleasant experiences and are problematic for children. Speech Inversion (SI), a noninvasive technique, offers a promising alternative for estimating articulatory movement without the need for physical instrumentation. In this study, an SI system trained on nasalance data from healthy adults is augmented with source information from electroglottography and acoustically derived F0, periodic and aperiodic energy estimates as proxies for glottal control. This model achieves 16.92% relative improvement in Pearson Product-Moment Correlation (PPMC) compared to a previous SI system for nasalance estimation. To adapt the SI system for nasalance estimation in children with Velopharyngeal Insufficiency (VPI), the model initially trained on adult speech was fine-tuned using children with VPI data, yielding an 7.90% relative improvement in PPMC compared to its performance before fine-tuning.

Figures

Figures reproduced from arXiv: 2509.09489 by the authors.

**Figure 2.** Proposed model architecture for SI system. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Waveforms, envelope of oral and nasal signals and spectrogram are [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Waveforms, envelope of oral and nasal signals and spectrogram of [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Towards Language-Agnostic Speech Inversion
eess.AS 2026-07 conditional novelty 5.0 of 10

An SI system trained only on American English estimates oral tract variables and source features on unseen French and Russian with PPMC 0.83 and 0.74, and also tracks velopharyngeal opening via nasalance.

Reference graph

Works this paper leans on

26 extracted references · 4 linked inside Pith · cited by 1 Pith paper

[5]

Speaker-independent speech in- version for recovery of velopharyngeal port constriction degree,

Y . M. Siriwardena, S. E. Boyce, M. K. Tiede, L. Oren, B. Fletcher, M. Stern, and C. Y . Espy-Wilson, “Speaker-independent speech in- version for recovery of velopharyngeal port constriction degree,”The Journal of the Acoustical Society of America, vol. 156, no. 2, pp. 1380– 1390, 2024

2024
[1]

Phonics: A large phoneme-grapheme frequency count revised,

E. Fry, “Phonics: A large phoneme-grapheme frequency count revised,” Journal of Literacy Research, vol. 36, no. 1, pp. 85–98, 2004

2004
[2]

Anticipatory and carryover effects: Implications for models of speech production,

F. Bell-Berti, R. A. Krakow, C. E. Gelfer, and S. E. Boyce, “Anticipatory and carryover effects: Implications for models of speech production,” Producing speech: contemporary issues for Katherine Safford Harris, pp. 77–98, 1995

1995
[3]

The reliability of visual ratings of velopharyngeal physiology for speech,

T. Watterson, J. Wendel, L. M. Grames, and A. Warner, “The reliability of visual ratings of velopharyngeal physiology for speech,”The Cleft Palate-Craniofacial Journal, vol. 58, no. 5, pp. 546–556, 2021

2021
[4]

A. W. Kummer,Cleft palate and craniofacial conditions: A comprehen- sive guide to clinical management: A Comprehensive guide to clinical management. Jones & Bartlett Learning, 2018

2018
[6]

Wav2nas: An exploratory approach to nasalance estimation in speech,

R. Feng, Y .-A. Chen, Y .-L. Liu, J.-H. Yuan, and Z.-H. Ling, “Wav2nas: An exploratory approach to nasalance estimation in speech,” in2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024, pp. 1–5

2024
[7]

Speaker-independent speech inversion for estimation of nasalance,

Y . M. Siriwardena, C. Espy-Wilson, S. Boyce, M. K. Tiede, and L. Oren, “Speaker-independent speech inversion for estimation of nasalance,” arXiv preprint arXiv:2306.00203, 2023

arXiv 2023
[8]

Acoustic-to- articulatory speech inversion with multi-task learning,

Y . M. Siriwardena, G. Sivaraman, and C. Espy-Wilson, “Acoustic-to- articulatory speech inversion with multi-task learning,”arXiv preprint arXiv:2205.13755, 2022

arXiv 2022

Show all 26 references

[9]

Evidence of vocal tract articulation in self-supervised learning of speech,

C. J. Cho, P. Wu, A. Mohamed, and G. K. Anumanchipalli, “Evidence of vocal tract articulation in self-supervised learning of speech,” inICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5

2023
[10]

Improving speech inversion through self-supervised embeddings and enhanced tract variables,

A. A. Attia, Y . M. Siriwardena, and C. Espy-Wilson, “Improving speech inversion through self-supervised embeddings and enhanced tract variables,” in2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024, pp. 306–310

2024
[11]

Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasality,

S. Tabatabaee, S. Boyce, L. Oren, M. Tiede, and C. Espy-Wilson, “Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasality,” inInterspeech 2025, 2025, pp. 325–329

2025
[12]

The secret source: Incorporat- ing source features to improve acoustic-to-articulatory speech inversion,

Y . M. Siriwardena and C. Espy-Wilson, “The secret source: Incorporat- ing source features to improve acoustic-to-articulatory speech inversion,” inICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5

2023
[13]

FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments ,

S. Tabatabaee, J. Liu, and C. Espy-Wilson, “FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments ,” inInterspeech 2025, 2025, pp. 2815–2819

2025
[14]

Introduction to partial fine-tuning: A compre- hensive evaluation of end-to-end children’s automatic speech recognition adaptation,

T. Rolland and A. Abad, “Introduction to partial fine-tuning: A compre- hensive evaluation of end-to-end children’s automatic speech recognition adaptation,”Procs. of Interspeech, Kos Island, Greece, pp. 5178–5182, 2024

2024
[15]

Acoustic-to-articulatory speech inversion features for mispronunciation detection of/r/in child speech sound dis- orders,

N. R. Benway, Y . M. Siriwardena, J. L. Preston, E. Hitchcock, T. McAl- lister, and C. Espy-Wilson, “Acoustic-to-articulatory speech inversion features for mispronunciation detection of/r/in child speech sound dis- orders,”arXiv preprint arXiv:2305.16085, 2023

2023 arXiv
[16]

Subtyping speech errors in childhood speech sound disorders with acoustic-to-articulatory speech inversion,

N. R. Benway, S. Tabatabaee, B. Munson, J. Preston, and C. Espy- Wilson, “Subtyping speech errors in childhood speech sound disorders with acoustic-to-articulatory speech inversion,” inProc. Interspeech 2025, 2025, pp. 2800–2804

2025
[17]

Examining vocal tract coordination in childhood apraxia of speech with acoustic-to- articulatory speech inversion feature sets,

N. R. Benway, J. L. Preston, and C. Espy-Wilson, “Examining vocal tract coordination in childhood apraxia of speech with acoustic-to- articulatory speech inversion feature sets,” inProceedings of the Annual Conference of the International Speech Communication Association, INTE...

2024
[18]

Perceptual ratings predict speech inversion articula- tory kinematics in childhood speech sound disorders,

N. R. Benway, S. Tabatabaee, D. Wang, B. Munson, J. L. Preston, and C. Espy-Wilson, “Perceptual ratings predict speech inversion articula- tory kinematics in childhood speech sound disorders,”arXiv preprint arXiv:2507.01888, 2025

2025 arXiv
[19]

Motor speech disorders, saunders w,

F. Darley, A. Aronsnon, and J. Brown, “Motor speech disorders, saunders w,”B, Philadelphia, pp. 171–975, 1975

1975
[20]

Ieee recommended practice for speech quality mea- surements,

E. H. Rothauser, “Ieee recommended practice for speech quality mea- surements,”IEEE Transactions on Audio and Electroacoustics, vol. 17, no. 3, pp. 225–246, 1969

1969
[21]

R. A. Krakow,The articulatory organization of syllables: A kinematic analysis of labial and velar gestures. Yale University, 1989

1989
[22]

Speech production database user’s handbook,

J. R. Westbury, “Speech production database user’s handbook,”IEEE Personal Communications-IEEE Pers. Commun., vol. 0, no, 1994

1994
[23]

Use of temporal information: Detection of periodicity, aperiodicity, and pitch in speech,

O. Deshmukh, C. Y . Espy-Wilson, A. Salomon, and J. Singh, “Use of temporal information: Detection of periodicity, aperiodicity, and pitch in speech,”IEEE Transactions on Speech and Audio Processing, vol. 13, no. 5, pp. 776–786, 2005

2005
[24]

Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”IEEE/ACM transactions on audio, speech, and language processing, vol. 29, pp. 3451–3460, 2021

2021
[25]

wav2vec 2.0: A framework for self-supervised learning of speech representations,

A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in neural information processing systems, vol. 33, pp. 12 449– 12 460, 2020

2020
[26]

Wavlm: Large-scale self-supervised pre- training for full stack speech processing,

S. Chen, C. Wang, Z. Chen, Y . Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiaoet al., “Wavlm: Large-scale self-supervised pre- training for full stack speech processing,”IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1505–1518, 2022

2022

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [5]

Speaker-independent speech in- version for recovery of velopharyngeal port constriction degree,

Y . M. Siriwardena, S. E. Boyce, M. K. Tiede, L. Oren, B. Fletcher, M. Stern, and C. Y . Espy-Wilson, “Speaker-independent speech in- version for recovery of velopharyngeal port constriction degree,”The Journal of the Acoustical Society of America, vol. 156, no. 2, pp. 1380– 1390, 2024

2024

[2] [1]

Phonics: A large phoneme-grapheme frequency count revised,

E. Fry, “Phonics: A large phoneme-grapheme frequency count revised,” Journal of Literacy Research, vol. 36, no. 1, pp. 85–98, 2004

2004

[3] [2]

Anticipatory and carryover effects: Implications for models of speech production,

F. Bell-Berti, R. A. Krakow, C. E. Gelfer, and S. E. Boyce, “Anticipatory and carryover effects: Implications for models of speech production,” Producing speech: contemporary issues for Katherine Safford Harris, pp. 77–98, 1995

1995

[4] [3]

The reliability of visual ratings of velopharyngeal physiology for speech,

T. Watterson, J. Wendel, L. M. Grames, and A. Warner, “The reliability of visual ratings of velopharyngeal physiology for speech,”The Cleft Palate-Craniofacial Journal, vol. 58, no. 5, pp. 546–556, 2021

2021

[5] [4]

A. W. Kummer,Cleft palate and craniofacial conditions: A comprehen- sive guide to clinical management: A Comprehensive guide to clinical management. Jones & Bartlett Learning, 2018

2018

[6] [6]

Wav2nas: An exploratory approach to nasalance estimation in speech,

R. Feng, Y .-A. Chen, Y .-L. Liu, J.-H. Yuan, and Z.-H. Ling, “Wav2nas: An exploratory approach to nasalance estimation in speech,” in2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024, pp. 1–5

2024

[7] [7]

Speaker-independent speech inversion for estimation of nasalance,

Y . M. Siriwardena, C. Espy-Wilson, S. Boyce, M. K. Tiede, and L. Oren, “Speaker-independent speech inversion for estimation of nasalance,” arXiv preprint arXiv:2306.00203, 2023

arXiv 2023

[8] [8]

Acoustic-to- articulatory speech inversion with multi-task learning,

Y . M. Siriwardena, G. Sivaraman, and C. Espy-Wilson, “Acoustic-to- articulatory speech inversion with multi-task learning,”arXiv preprint arXiv:2205.13755, 2022

arXiv 2022